Allometric relationships for the linear dimensions and biomass of Puerto Rican mangroves
| Authors: | Charles A. Price, Todd A. Schroeder, Benjamin Branoff, Skip J. Van Bloem, Nicole Pillot-Torres, Morgan H. Chaudry, Monica Papeş, Humfredo Marcano-Vega |
| Year: | 2025 |
| Type: | Scientific Journal |
| Station: | Southern Research Station |
| DOI: | https://doi.org/10.1016/j.foreco.2025.122908 |
| Source: | Forest Ecology and Management |
Abstract
Accurate biomass estimation is essential for carbon stock assessment and coastal ecosystem management. Despite decades of research and interest in Puerto Rican mangroves, we lack species specific allometric models with only 10 Rhizophora mangleindividuals having been collected previously. To address this gap, we developed allometric equations for four mangrove species (Avicennia germinans, Laguncularia racemosa, Rhizophora mangle, and Conocarpus erectus) by harvesting and analyzing 152 individuals. Biomass estimation models were developed using standardized major axis (SMA) regression, incorporating DBH, tree height, crown diameter, and wet mass. Analysis of bivariate relationships between the linear dimensions and mass reveal common allometric slopes between species in 5 out of the 6 relationships. Local equations had a lower RMSE than global models, with DBH-based equations achieving the highest predictive accuracy (R² = 0.94), while crown diameter showed the weakest correlation (R² = 0.74). At the species level, DBH was the best predictor of both wet and dry mass in all cases examined. These results enhance biomass estimation accuracy for Puerto Rican mangroves, providing a critical tool for carbon stock assessments, conservation planning, and regional ecosystem management strategies.
Future allometric harvesting efforts should focus on filling gaps in existing data by sampling new areas and by increasing sample sizes to capture more variability. When collecting allometric data, researchers should collect trees across the entire range of available sizes. Collecting allometric data from a broad range of forest types and environments will help to understand the extent of morphological and allometric variability.